Serverless Data Processing. A large bulk of
previous works have proposed high-level frameworks for running large-scale analytics on serverless functions. For example, PyWren [1]}, IBM-PyWren[2]} and Lithops [3]}, [4]} are map-reduce frameworks running over FaaS executors that take advantage of object storage to store intermediate data. Lithops [3]}, [4]} is multi-cloud and also implements the
native multiprocessing module available in Python to enable the transparent execution of multiprocessing applications over FaaS platforms. Further,
gg [7]} is a library that uses AWS Lambda for CPU-bound intensive jobs such as video-encoding. Numpywren [8]} is an elastic linear
algebra library on top of a pure serverless architecture. Starling [9]} proposes a serverless query execution engine.
Serverless ML systems, including MLLess, build upon the lessons learned from these works to increase their performance and cost-efficiency.
